import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


__a    :	Optional[int]         =    False




@skip_mps
class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Tuple     					=		StableDiffusionAttendAndExcitePipeline
			__a    :					List[Any]     					=		False
			__a    :					Union[str, Any]     					=		TEXT_TO_IMAGE_PARAMS
			__a    :					Optional[int]     					=		TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}					)
			__a    :					str     					=		TEXT_TO_IMAGE_IMAGE_PARAMS
			__a    :					int     					=		TEXT_TO_IMAGE_IMAGE_PARAMS





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls		)	->   int:

							'''simple docstring'''


							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__		)





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls		)	->   Optional[int]:

							'''simple docstring'''


							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							torch.manual_seed(0		)
							__lowercase					     =				UNetaDConditionModel(
							    block_out_channels=(32, 64) ,  layers_per_block=1 ,  sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,  cross_attention_dim=32 ,  attention_head_dim=(2, 4) ,  use_linear_projection=lowerCAmelCase__ ,  )
							__lowercase					     =				DDIMScheduler(
							    beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  clip_sample=lowerCAmelCase__ ,  set_alpha_to_one=lowerCAmelCase__ ,  )
							torch.manual_seed(0		)
							__lowercase					     =				AutoencoderKL(
							    block_out_channels=[32, 64] ,  in_channels=3 ,  out_channels=3 ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,  latent_channels=4 ,  sample_size=1_28 ,  )
							torch.manual_seed(0		)
							__lowercase					     =				CLIPTextConfig(
							    bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=32 ,  intermediate_size=37 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=10_00 ,  hidden_act='''gelu''' ,  projection_dim=5_12 ,  )
							__lowercase					     =				CLIPTextModel(lowerCAmelCase__		)
							__lowercase					     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

							__lowercase					     =				{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}

							return components





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=0		)	->   Union[str, Any]:

							'''simple docstring'''


							if str(lowerCAmelCase__		).startswith('''mps'''		):
											__lowercase					     =				torch.manual_seed(lowerCAmelCase__		)
							else:
											__lowercase					     =				torch.Generator(device=lowerCAmelCase__		).manual_seed(lowerCAmelCase__		)
							__lowercase					     =				__lowercase					     =				{
							    '''prompt''': '''a cat and a frog''',
							    '''token_indices''': [2, 5],
							    '''generator''': generator,
							    '''num_inference_steps''': 1,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							    '''max_iter_to_alter''': 2,
							    '''thresholds''': {0: 0.7},
							}
							return inputs





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				'''cpu'''

							__lowercase					     =				self.get_dummy_components()
							__lowercase					     =				self.pipeline_class(**lowerCAmelCase__		)
							pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

							__lowercase					     =				self.get_dummy_inputs(lowerCAmelCase__		)
							__lowercase					     =				pipe(**lowerCAmelCase__		).images
							__lowercase					     =				image[0, -3:, -3:, -1]

							self.assertEqual(image.shape ,  (1, 64, 64, 3)		)
							__lowercase					     =				np.array(
							    [0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496]		)
							__lowercase					     =				np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(lowerCAmelCase__ ,  1E-3		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							self._test_inference_batch_single_identical(batch_size=2 ,  expected_max_diff=7E-4		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							super().test_save_load_local(expected_max_difference=5E-4		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							super().test_save_load_optional_components(expected_max_difference=4E-4		)




@require_torch_gpu
@slow
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls		)	->   int:

							'''simple docstring'''


							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__		)





			@classmethod
			def 			_SCREAMING_SNAKE_CASE     (  cls		)	->   Optional[int]:

							'''simple docstring'''


							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				torch.manual_seed(51		)

							__lowercase					     =				StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    '''CompVis/stable-diffusion-v1-4''' ,  safety_checker=lowerCAmelCase__ ,  torch_dtype=torch.floataa		)
							pipe.to('''cuda'''		)

							__lowercase					     =				'''a painting of an elephant with glasses'''
							__lowercase					     =				[5, 7]

							__lowercase					     =				pipe(
							    prompt=lowerCAmelCase__ ,  token_indices=lowerCAmelCase__ ,  guidance_scale=7.5 ,  generator=lowerCAmelCase__ ,  num_inference_steps=5 ,  max_iter_to_alter=5 ,  output_type='''numpy''' ,  ).images[0]

							__lowercase					     =				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'''		)
							assert np.abs((expected_image - image).max()		) < 5E-1
import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Union[str, Any]         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				if "model" in sd.keys():
								__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)['''model''']

				# pop unnecessary weights
				__lowercase					     =				[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(lowercase				)

				__lowercase					     =				{
				    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
				    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
				    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
				    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												__lowercase					     =				sd.pop(lowercase				)

				__lowercase					     =				list(sd.keys()				)
				for key in keys:
								if ".qkv_proj." in key:
												__lowercase					     =				sd[key]
												# We split QKV in separate Q,K,V

												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.q_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.k_proj.'''				)
												__lowercase					     =				key.replace('''.qkv_proj.'''				,     '''.v_proj.'''				)

												__lowercase					     =				value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												__lowercase     ,	__lowercase     ,	__lowercase					     =				torch.split(lowercase				,     depth // 3				,     dim=0				)

												__lowercase					     =				q
												__lowercase					     =				k
												__lowercase					     =				v
												del sd[key]

				return sd





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				load_checkpoint(lowercase				)

				if config is not None:
								__lowercase					     =				OPTConfig.from_pretrained(lowercase				)
				else:
								__lowercase					     =				OPTConfig()

				__lowercase					     =				OPTModel(lowercase				).half().eval()
				model.load_state_dict(lowercase				)

				# Check results
				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--fairseq_path""",
		    type=str,
		    help=(
		        """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
		        """ https://huggingface.co/models?other=opt_metasq"""
		    ),
		)
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
		__a    :	Any         =    parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)